Recently (~10 days ago) my autodetected workouts suddenly stopped registering consistently and my fitbit stopped doing the daily celebration thing at 10,000 steps. It's not that big of a deal but I kind of liked it. Anybody know whether this can be turned back on with some niche setting or update? For the record, I currently have workout autodetection enabled for all exercises and I do have a step goal as well.

As a full disclaimer, I'm currently working on the dev team of Vora, a new health fitness app. But this is not an app promotion post, I just want to share a new tool we created on our website to help people choose the best wearable out of the hundreds out there. We got a lot of questions from our users about which devices are good and which ones are bad, and which devices track which metrics, so we created this to help people figure it out.

There's no download or payment hidden at all to use this, and we're not paid by any of these device companies either, these are just ratings based off of the accuracy of these devices based off of our research and publicly available data.

The tool ranks each wearable gadget based on how effectively it tracks various factors, such as VO2 Max and SPO2, and gives you info on how much it costs and which health metrics you are missing with different wearable devices. It also ranks several dozen types of wearables per category (sleep, longevity, etc) and gives you the best performer for each category. This tool also gives info on what exactly different metrics mean, and how combinations of different wearable devices function compared to individual ones. And you don't need to download anything or give any personal info to use this tool, it's completely free.

Would love to hear any feedback on this tool, and please let me know if this tool is helpful or any improvements we could make. Also if there are any wearables I missed, please let me know and I'll try to do some research and add the info. Here's the link to the tool if you want to try it out.

There are so many wearables out now and they all give vastly different metrics and scores for sleep, but why do they output completely different sleep staging and recovery scores?

It comes down to 2 things: Hardware and proprietary algorithmic weighting**.** Here is the technical breakdown.

1. The Hardware & Calibration Problem

To understand why they are different you have to understand the three major sensors. All major wearables rely on a three-part sensor suite to infer sleep architecture:

• Photoplethysmography (PPG): Optical sensors that shoot light into peripheral capillaries (network of arteries, veins, and capillaries) to track blood volume changes. This extracts Resting Heart Rate (RHR) and Heart Rate Variability (HRV).
• 3-Axis Actigraphy: Accelerometers tracking 3D movement to differentiate gross physical motion (tossing/turning) from immobility which can be used to track the circadian rhythms (body's own internal clock). Telling the device when you are asleep and awake, also the reason these devices can’t track naps very well. 
• Peripheral Biosensors: A very unique sensor that has to recognize elements like cells or sweat and look for changes or signals in them like heat or blood with a transducer creating data of body temperature or SpO2 shifts.

Why calibration takes weeks: The device must establish your specific baseline autonomic nervous system activity and circadian rhythm. Because temperature drops and cardiac deceleration are highly individualized, the algorithm requires longitudinal data to isolate true physiological shifts from baseline noise.

2. Proprietary Algorithmic Weighting

Because wearables can’t read brainwaves, sleep staging is an act of mathematical inference. Every company uses a different machine learning model to weight the data:

• Whoop (The Athletic Model): Heavily biases HRV (specifically parasympathetic tone recovery). Designed for athletes, a drop in HRV heavily penalizes your score, even if sleep duration was long.
• Fitbit (The Big-Data Heuristic Model): Leverages Google’s population-level datasets. It excels at pattern-matching by comparing your real-time telemetry against millions of historical user profiles.
• Oura (The Thermal Model): Utilizes its finger-placement advantage due to the minimal layers with the tissue and fewer interfering tendons that results in cleaner data with the PPG and biosensor. Oura places a mathematical weight on nocturnal skin temperature and vascular shifts, making it highly sensitive to the vasodilation changes that correlate with REM sleep.
• Third Party Apps (Sensor-Fusion Model):  Third-party apps like vora health act as data middleware via APIs like Apple HealthKit or Google Health Connect. Instead of treating all data equally, they use weighting specific biometrics based on what each hardware device does best (e.g., trusting Oura for temperature, Whoop for high-frequency HRV) to find correlations rather than raw detections.

The Clinical Bottom Line: While consumer wearables are excellent for long-term trend tracking, they are proxy metrics. For diagnostic and severe issues like sleep apnea, a sleep study using Polysomnography (PSG) remains the gold standard.It directly measures cortical biopotentials (neurons in the brain), muscle atonia (EMG) as well as others like heart rate, oxygen levels, breathing patterns. All of this done by experts to give definitive answers on any major health concerns but if you just like to track your health then the devices are great of you

There are so many wearable out now and they all give vastly different metrics and scores for sleep, but why do they output completely different sleep staging and recovery scores?

It comes down to 2 things: Hardware and proprietary algorithmic weighting**.** Here is the technical breakdown.

1. The Hardware & Calibration Problem

To understand why they are different you have to understand the three major sensors. All major wearable rely on a three-part sensor suite to infer sleep architecture:

• Photoplethysmography (PPG): Optical sensors that shoot light into peripheral capillaries (network of arteries, veins, and capillaries) to track blood volume changes. This extracts Resting Heart Rate (RHR) and Heart Rate Variability (HRV).
• 3-Axis Actigraphy: Accelerometers tracking 3D movement to differentiate gross physical motion (tossing/turning) from immobility which can be used to track the circadian rhythms (body's own internal clock). Telling the device when you are asleep and awake, also the reason these devices can’t track naps very well. 
• Peripheral Biosensors: A very unique sensor that has to recognize elements like cells or sweat and look for changes or signals in them like heat or blood with a transducer creating data of body temperature or SpO2 shifts.

Why calibration takes weeks: The device must establish your specific baseline autonomic nervous system activity and circadian rhythm. Because temperature drops and cardiac deceleration are highly individualized, the algorithm requires longitudinal data to isolate true physiological shifts from baseline noise.

2. Proprietary Algorithmic Weighting

Because wearables can’t read brainwaves, sleep staging is an act of mathematical inference. Every company uses a different machine learning model to weight the data:

• Whoop (The Athletic Model): Heavily biases HRV (specifically parasympathetic tone recovery). Designed for athletes, a drop in HRV heavily penalizes your score, even if sleep duration was long.
• Fitbit (The Big-Data Heuristic Model): Leverages Google’s population-level datasets. It excels at pattern-matching by comparing your real-time telemetry against millions of historical user profiles.
• Oura (The Thermal Model): Utilizes its finger-placement advantage due to the minimal layers with the tissue and fewer interfering tendons that results in cleaner data with the PPG and biosensor. Oura places a mathematical weight on nocturnal skin temperature and vascular shifts, making it highly sensitive to the vasodilation changes that correlate with REM sleep.
• Third Party Apps (Sensor-Fusion Model):  Third-party apps like vora health act as data middleware via APIs like Apple HealthKit or Google Health Connect. Instead of treating all data equally, they use weighting specific biometrics based on what each hardware device does best (e.g., trusting Oura for temperature, Whoop for high-frequency HRV) to find correlations rather than raw detections.

The Clinical Bottom Line: While consumer wearables are excellent for long-term trend tracking, they are proxy metrics. For diagnostic and severe issues like sleep apnea, a sleep study using Polysomnography (PSG) remains the gold standard. It directly measures cortical biopotentials (neurons in the brain), muscle atonia (EMG) as well as others like heart rate, oxygen levels, breathing patterns. All of this done by experts to give definitive answers on any major health concerns but if you just like to track your health then the devices are great of you

I Just got a Garmin the other day and having been trying to figure out what all the different data means. I download Vora to help me with my workouts, and its been nice but I have no clue what intensity minutes or body battery means like it just gives me a number with no context of if its good or bad.